title: Fuzzy central tendency measure for time series variability analysis with application to fatigue electromyography signals.
abstract: A new method, namely fuzzy central tendency measure (fCTM) analysis, that could enable measurement of the variability of a time series, is presented in this study. Tests on simulated data sets show that fCTM is superior to the conventional central tendency measure (CTM) in several respects, including improved relative consistency and robustness to noise. The proposed fCTM method was applied to electromyograph (EMG) signals recorded during sustained isometric contraction for tracking local muscle fatigue. The results showed that the fCTM increased significantly during the development of muscle fatigue, and it was more sensitive to the fatigue phenomenon than mean frequency (MNF), the most commonly-used muscle fatigue indicator.
authors.forename: Hong-Bo
authors.initials: HB
authors.lastname: Xie
authors.forename: Socrates
authors.initials: S
authors.lastname: Dokos
mesh_terms: D000328:Adult; D000465:Algorithms; D004566:Electrodes; D004576:Electromyography; D006801:Humans; D007537:Isometric Contraction / Q000502:physiology; D018763:Muscle Fatigue / Q000502:physiology*; D059629:Signal-To-Noise Ratio